Plots_inotec_data
Joao Marreiuros and David Nora
2021-07-27 16:02:43
Goal of the script
This script plots all sensor data in order to visualizes the measurements recorded throughout the tool function experiment. In this study the variable of interest is the Penetration depth
dir_in <- "../derived_data"
dir_out <- "../plots"Raw data must be located in ~/../derived_data.
Formatted data will be saved in ~/../plots. The knit directory for this script is the project directory.
Load packages
library(R.utils)
library(ggplot2)
library(tools)
library(tidyverse)
library(patchwork)
library(doBy)
library(ggrepel)
library(openxlsx)Get name, path and information of the file
data_file <- list.files(dir_in, pattern = "\\.Rbin$", full.names = TRUE)
md5_in <- md5sum(data_file)
info_in <- data.frame(file = basename(names(md5_in)), checksum = md5_in, row.names = NULL)
info_inLoad data into R object
imp_data <- loadObject(data_file)
str(imp_data)'data.frame': 114706 obs. of 10 variables:
$ Sample : chr "DAC3-2" "DAC3-2" "DAC3-2" "DAC3-2" ...
$ Raw_material : chr "Dacite" "Dacite" "Dacite" "Dacite" ...
$ Contact_material: chr "wood" "wood" "wood" "wood" ...
$ Stroke : num 1 1 1 1 1 1 1 1 1 1 ...
$ Step : num 1 2 3 4 5 6 7 8 9 10 ...
$ Force : num -58.8 -59.3 -61.6 -56.7 -58 ...
$ Friction : num -2.46 -9.88 -31.78 -53.99 -64.34 ...
$ Depth : num 13.8 13.8 13.6 13.3 13.1 ...
$ Position : num 260 263 297 356 387 ...
$ Velocity : num -0.0031 106.7299 502.972 551.2161 162.7834 ...# replace "stroke" by "cycle"
colnames(imp_data)[colnames(imp_data) == "Stroke"] <- "Cycle"The imported file is: “~/../derived_data/sampl.Rbin”
Plot each of the selected numeric variable
Plot showing the absolut penetration depths
# calculates the absolute depths reached per sample
abs.depth <- function(x) {
noNA <- x[!is.na(x)]
out <- abs(min(noNA) - max(noNA))
}
# Define grouping variable and compute the summary statistics
depth <- summaryBy(Depth ~ Sample+Raw_material+Contact_material,
data=imp_data,
FUN=abs.depth)
str(depth)'data.frame': 12 obs. of 4 variables:
$ Sample : chr "DAC3-2" "DAC3-4" "DAC3-6" "FLT10-2" ...
$ Raw_material : chr "Dacite" "Dacite" "Dacite" "Flint" ...
$ Contact_material: chr "wood" "wood" "wood" "wood" ...
$ Depth.abs.depth : num 2.94 2.5 2.91 2.88 3.89 ...depth[["Contact_material"]] <- factor(depth[["Contact_material"]])
# plots all depth points in one facet plot (contact material together)
p3 <- ggplot(data = depth, aes(x = Contact_material,
y = Depth.abs.depth, colour =
Raw_material)) +
geom_point() + labs(y = "Absolute depth (mm)") +
facet_wrap(~Raw_material, strip.position = "bottom") +
# avoids overplotting of the labels (sample IDs)
geom_text_repel(aes(label=Sample), size = 2,
nudge_x = -0.4,
segment.size = 0.1, force = 2,
seed = 123) +
scale_y_continuous(trans = "reverse") +
scale_x_discrete(position ="top") +
# removes the "_" between "Contact_material in the legend
labs(x = "Contact material") +
theme_classic() +
theme(legend.position = "none")
print(p3)
# save to PDF
file_out <- paste0(file_path_sans_ext(info_in[["file"]]),
"_depth_a_plot_", ".pdf")
ggsave(filename = file_out, plot = p3, path = dir_out,
device = "pdf",
width = 25, height = 17, units = "cm")
depth[["Raw_material"]] <- factor(depth[["Raw_material"]])
# plots all depth points in one facet plot (contact material separated)
p4 <- ggplot(data = depth, aes(x = Contact_material,
y = Depth.abs.depth, colour =
Raw_material)) +
geom_point() + labs(y = "Absolute depth (mm)") +
# avoids overplotting of the labels (sample IDs)
geom_text_repel(aes(label=Sample), size = 2,
nudge_x = -0.4,
segment.size = 0.1, force = 2,
seed = 123) +
scale_y_continuous(trans = "reverse") +
scale_x_discrete(position ="top") +
# removes the "_" between "Contact_material in the legend
labs(x = "Contact material") +
theme_classic() +
theme(axis.text.x = element_blank(), axis.ticks = element_blank()) +
theme(legend.position = "none")
print(p4)
# save to PDF
file_out <- paste0(file_path_sans_ext(info_in[["file"]]),
"_depth_b_plot_", ".pdf")
ggsave(filename = file_out, plot = p4, path = dir_out,
device = "pdf",
width = 25, height = 17, units = "cm")All sensor data
sp <- split(imp_data, imp_data[["Sample"]])
for (i in seq_along(sp)) {
# creates a sequence of every ~ 50th strokes
seq_st <- seq(1, length(unique(sp[[i]][["Cycle"]])), by = 40) %>%
c(max(unique(sp[[i]][["Cycle"]])))
dat_i_all <- sp[[i]] %>%
filter(Cycle %in% seq_st)
range_force_all <- range(dat_i_all[["Force"]])
range_friction_all <- range(dat_i_all[["Friction"]])
range_depth_all <- range(dat_i_all[["Depth"]])
range_velocity_all <- range(dat_i_all[["Velocity"]])
p1b <- ggplot(data = dat_i_all) +
geom_line(aes(x = Step, y = Force, colour = Cycle, group = Cycle), alpha = 0.3) +
labs(x = "Step", y = "Force [N]") +
scale_colour_continuous(trans = "reverse") +
coord_cartesian(ylim = range_force_all) +
scale_x_continuous(breaks=c(1, 4, 7, 10, 15, 20, 25)) +
theme_classic()
print(p1b)
p2b <- ggplot(data = dat_i_all) +
geom_line(aes(x = Step, y = Friction, colour = Cycle, group = Cycle), alpha = 0.3) +
labs(x = "Step", y = "Friction [N]") +
scale_colour_continuous(trans = "reverse") +
coord_cartesian(ylim = range_friction_all) +
scale_x_continuous(breaks=c(1, 4, 7, 10, 15, 20, 25)) +
theme_classic()
print(p2b)
p3b <- ggplot(data = dat_i_all) +
geom_line(aes(x = Step, y = Depth, colour = Cycle, group = Cycle), alpha = 0.3) +
labs(x = "Step", y = "Depth [mm]") +
scale_colour_continuous(trans = "reverse") +
coord_cartesian(ylim = range_depth_all) +
scale_x_continuous(breaks=c(1, 4, 7, 10, 15, 20, 25)) +
theme_classic()
print(p3b)
p4b <- ggplot(data = dat_i_all) +
geom_line(aes(x = Step, y = Velocity, colour = Cycle, group = Cycle), alpha = 0.3) +
labs(x = "Step", y = "Velocity [mm/s]") +
scale_colour_continuous(trans = "reverse") +
coord_cartesian(ylim = range_velocity_all) +
scale_x_continuous(breaks=c(1, 4, 7, 10, 15, 20, 25)) +
theme_classic()
print(p4b)
# patchwork plot
pb <- p1b + p2b + p3b + p4b + plot_annotation(title = names(sp)[i]) + plot_layout(ncol = 1, guides = "collect")
print(pb)
# save to PDF
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_sensors_plot_",
names(sp)[i], ".pdf")
ggsave(filename = file_out, plot = pb, path = dir_out, device = "pdf")
} 
Penetration depth plots showing the strokes as lines
# plots all strokes per sample divided by 40
# splits the data in the individual 24 samples
sp <- split(imp_data, imp_data[["Sample"]])
for (i in seq_along(sp)) {
# creates a sequence of every ~ 50th cycles
seq_st <- seq(1, length(unique(sp[[i]][["Cycle"]])), by = 40) %>%
c(max(unique(sp[[i]][["Cycle"]])))
dat_i_all <- sp[[i]] %>%
filter(Cycle %in% 1:500)
range_depth <- range(dat_i_all[["Depth"]])
p1 <- ggplot(data = dat_i_all, aes(x = Step, y = Depth, colour = Cycle)) +
geom_line(aes(group = Cycle), alpha = 0.3) +
labs(x = "Step", y = "Depth (mm)") + ylab(NULL) +
# reverses the legend starting with 0 going to 2000 strokes
scale_colour_continuous(trans = "reverse") +
coord_cartesian(ylim = range_depth) +
# changes the 'Step-number' in the x-legend
theme_classic()
# plots only the first 125 cycles per sample
dat_i_250 <- sp[[i]] %>%
# takes only the first 50 cycles per sample
filter(Cycle %in% 1:125)
range_depth <- range(dat_i_all[["Depth"]])
p2 <- ggplot(data = dat_i_250) +
geom_line(aes(x = Step, y = Depth, colour = Cycle, group = Cycle), alpha = 0.3) +
labs(x = "Step", y = "Depth (mm)") +
scale_colour_continuous(trans = "reverse") +
coord_cartesian(ylim = range_depth) +
theme_classic()
# plots only between 125 to 205 cycles per sample
dat_i_500 <- sp[[i]] %>%
# takes only the first 50 cycles per sample
filter(Cycle %in% 126:250)
range_depth <- range(dat_i_all[["Depth"]])
p3 <- ggplot(data = dat_i_500) +
geom_line(aes(x = Step, y = Depth, colour = Cycle, group = Cycle), alpha = 0.3) +
labs(x = "Step", y = "Depth (mm)") +
scale_colour_continuous(trans = "reverse") +
coord_cartesian(ylim = range_depth) +
theme_classic()
# plots only between 250 to 500 cycles per sample
dat_i_500 <- sp[[i]] %>%
# takes only the first 50 cycles per sample
filter(Cycle %in% 251:500)
range_depth <- range(dat_i_all[["Depth"]])
p4 <- ggplot(data = dat_i_500) +
geom_line(aes(x = Step, y = Depth, colour = Cycle, group = Cycle), alpha = 0.3) +
labs(x = "Step", y = "Depth (mm)") +
scale_colour_continuous(trans = "reverse") +
coord_cartesian(ylim = range_depth) +
theme_classic()
# patchwork plot
p <- p2 + p3 + p4 + p1 +plot_annotation(title = names(sp)[i])
print(p)
# save to PDF
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_depth_plot_",
names(sp)[i], ".pdf")
ggsave(filename = file_out, plot = p, path = dir_out,
device = "pdf")
}
The files will be saved as “~/../plots.[ext]”.
| # Save data ## Write to XLSX (summary statistics) |
r write.xlsx(list(depth = depth, depth_good = depth_good), file = paste0(dir_out, file_out, ".xlsx")) |
Error in buildWorkbook(x, asTable = asTable, ...): object 'depth_good' not found |
sessionInfo() and RStudio version
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] tools stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] openxlsx_4.2.4 ggrepel_0.9.1 doBy_4.6.11 patchwork_1.1.1
[5] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[9] readr_1.4.0 tidyr_1.1.3 tibble_3.1.2 tidyverse_1.3.1
[13] ggplot2_3.3.5 R.utils_2.10.1 R.oo_1.24.0 R.methodsS3_1.8.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.7 lubridate_1.7.10 curry_0.1.1
[4] lattice_0.20-44 assertthat_0.2.1 digest_0.6.27
[7] utf8_1.2.1 R6_2.5.0 cellranger_1.1.0
[10] backports_1.2.1 reprex_2.0.0 evaluate_0.14
[13] highr_0.9 httr_1.4.2 pillar_1.6.1
[16] rlang_0.4.11 readxl_1.3.1 rstudioapi_0.13
[19] Matrix_1.3-3 rmarkdown_2.9 labeling_0.4.2
[22] munsell_0.5.0 broom_0.7.8 compiler_4.1.0
[25] Deriv_4.1.3 modelr_0.1.8 xfun_0.24
[28] microbenchmark_1.4-7 pkgconfig_2.0.3 htmltools_0.5.1.1
[31] tidyselect_1.1.1 fansi_0.5.0 crayon_1.4.1
[34] dbplyr_2.1.1 withr_2.4.2 MASS_7.3-54
[37] grid_4.1.0 jsonlite_1.7.2 gtable_0.3.0
[40] lifecycle_1.0.0 DBI_1.1.1 magrittr_2.0.1
[43] scales_1.1.1 zip_2.2.0 cli_3.0.1
[46] stringi_1.6.2 farver_2.1.0 fs_1.5.0
[49] xml2_1.3.2 ellipsis_0.3.2 generics_0.1.0
[52] vctrs_0.3.8 glue_1.4.2 hms_1.1.0
[55] yaml_2.2.1 colorspace_2.0-2 rvest_1.0.0
[58] knitr_1.33 haven_2.4.1 END OF SCRIPT